Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PURPOSE

A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.

METHODS

A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.

RESULTS

The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.

CONCLUSION

This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

Evaluating Magnetic Seed Localization in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Receiving Neoadjuvant Systemic Therapy: A Comprehensive Review and Pooled Analysis

Background/Objectives: De-escalation of axillary surgery is made possible by advancements in both neoadjuvant systemic therapy (NST) and in localisation technology for breast lesions. Magseed®, developed in 2013 by Dr. Michael Douk of Cambridge, United Kingdom, is a wire-free localisation technology that facilitates the localisation and retrieval of lymph nodes for staging. Targeted axillary dissection (TAD), which entails marked lymph node biopsy (MLNB) and sentinel lymph node biopsy (SLNB), has emerged as the preferred method to assess residual disease in post-NST node-positive patients. This systematic review and pooled analysis evaluate the performance of Magseed® in TAD. Methods: The search was carried out in PubMed and Google Scholar. An assessment of localisation, retrieval rates, concordance between MLNB and SLNB, and pathological complete response (pCR) in clinically node-positive patients post NST was undertaken. Results: Nine studies spanning 494 patients and 497 procedures were identified, with a 100% successful deployment rate, a 94.2% (468/497) [95% confidence interval (CI), 93.7-94.7] localisation rate, a 98.8% (491/497) retrieval rate, and a 68.8% (247/359) [95% CI 65.6-72.0] concordance rate. pCR was observed in 47.9% (220/459) ) [95% CI 43.3-52.6] of cases. Subgroup analysis of studies reporting the pathological status of MLNB and SLNB separately revealed an FNR of 4.2% for MLNB and 17.6% for SLNB (p = 0.0013). Mean duration of implantation was 37 days (range: 0-188). Conclusions: These findings highlight magnetic seed localisation's efficacy in TAD for NST-treated node-positive patients, aiding in accurate axillary pCR identification and safe de-escalation of axillary surgery in excellent responders.

Artificial Intelligence in Senology - Where Do We Stand and What Are the Future Horizons?

Artificial Intelligence (AI) is defined as the simulation of human intelligence by a digital computer or robotic system and has become a hype in current conversations. A subcategory of AI is deep learning, which is based on complex artificial neural networks that mimic the principles of human synaptic plasticity and layered brain architectures, and uses large-scale data processing. AI-based image analysis in breast screening programmes has shown non-inferior sensitivity, reduces workload by up to 70% by pre-selecting normal cases, and reduces recall by 25% compared to human double reading. Natural language programs such as ChatGPT (OpenAI) achieve 80% and higher accuracy in advising and decision making compared to the gold standard: human judgement. This does not yet meet the necessary requirements for medical products in terms of patient safety. The main advantage of AI is that it can perform routine but complex tasks much faster and with fewer errors than humans. The main concerns in healthcare are the stability of AI systems, cybersecurity, liability and transparency. More widespread use of AI could affect human jobs in healthcare and increase technological dependency. AI in senology is just beginning to evolve towards better forms with improved properties. Responsible training of AI systems with meaningful raw data and scientific studies to analyse their performance in the real world are necessary to keep AI on track. To mitigate significant risks, it will be necessary to balance active promotion and development of quality-assured AI systems with careful regulation. AI regulation has only recently included in transnational legal frameworks, as the European Union's AI Act was the first comprehensive legal framework to be published, in December 2023. Unacceptable AI systems will be banned if they are deemed to pose a clear threat to people's fundamental rights. Using AI and combining it with human wisdom, empathy and affection will be the method of choice for further, fruitful development of tomorrow's senology.

Use of artificial intelligence in breast surgery: a narrative review

Background and Objective

We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application.

Methods

Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded.

Key Content and Findings

AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence.

Conclusions

Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.

State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

Early detection of breast cancer is crucial for a better prognosis. Various studies have been conducted where tumor lesions are detected and localized on images. This is a narrative review where the studies reviewed are related to five different image modalities: histopathological, mammogram, magnetic resonance imaging (MRI), ultrasound, and computed tomography (CT) images, making it different from other review studies where fewer image modalities are reviewed. The goal is to have the necessary information, such as pre-processing techniques and CNN-based diagnosis techniques for the five modalities, readily available in one place for future studies. Each modality has pros and cons, such as mammograms might give a high false positive rate for radiographically dense breasts, while ultrasounds with low soft tissue contrast result in early-stage false detection, and MRI provides a three-dimensional volumetric image, but it is expensive and cannot be used as a routine test. Various studies were manually reviewed using particular inclusion and exclusion criteria; as a result, 91 recent studies that classify and detect tumor lesions on breast cancer images from 2017 to 2022 related to the five image modalities were included. For histopathological images, the maximum accuracy achieved was around 99 % , and the maximum sensitivity achieved was 97.29 % by using DenseNet, ResNet34, and ResNet50 architecture. For mammogram images, the maximum accuracy achieved was 96.52 % using a customized CNN architecture. For MRI, the maximum accuracy achieved was 98.33 % using customized CNN architecture. For ultrasound, the maximum accuracy achieved was around 99 % by using DarkNet-53, ResNet-50, G-CNN, and VGG. For CT, the maximum sensitivity achieved was 96 % by using Xception architecture. Histopathological and ultrasound images achieved higher accuracy of around 99 % by using ResNet34, ResNet50, DarkNet-53, G-CNN, and VGG compared to other modalities for either of the following reasons: use of pre-trained architectures with pre-processing techniques, use of modified architectures with pre-processing techniques, use of two-stage CNN, and higher number of studies available for Artificial Intelligence (AI)/machine learning (ML) researchers to reference. One of the gaps we found is that only a single image modality is used for CNN-based diagnosis; in the future, a multiple image modality approach can be used to design a CNN architecture with higher accuracy.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis

BACKGROUND

This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients.

METHODS

We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included 'artificial intelligence', 'CT,' 'MRI', 'breast cancer' and 'lymph nodes'. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC).

RESULTS

A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79-0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66-0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0-4.8), a negative likelihood ratio of 0.18 (95% CI 0.13-0.26) and a diagnostic odds ratio of 25 (95% CI 17-38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79-0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69-0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7-7.0), a negative likelihood ratio of 0.15 (95% CI 0.08-0.27) and a diagnostic odds ratio of 30 (95% CI 12-72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82-0.88) and 0.91 (95% CI 0.88-0.93), respectively.

CONCLUSION

Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer

In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis.

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

The study aimed to compare the metastatic pattern of breast cancer and the intermodality proportion of agreement between [18F]FDG-PET/CT and CE-CT. Women with metastatic breast cancer (MBC) were enrolled prospectively and underwent a combined [18F]FDG-PET/CT and CE-CT scan to diagnose MBC. Experienced nuclear medicine and radiology physicians evaluated the scans blinded to the opposite scan results. Descriptive statistics were applied, and the intermodality proportion of agreement was used to compare [18F]FDG-PET/CT and CE-CT. In total, 76 women with verified MBC were enrolled in the study. The reported number of site-specific metastases for [18F]FDG-PET/CT vs. CE-CT was 53 (69.7%) vs. 44 (57.9%) for bone lesions, 31 (40.8%) vs. 43 (56.6%) for lung lesions, and 16 (21.1%) vs. 23 (30.3%) for liver lesions, respectively. The proportion of agreement between imaging modalities was 76.3% (95% CI 65.2-85.3) for bone lesions; 82.9% (95% CI 72.5-90.6) for liver lesions; 57.9% (95% CI 46.0-69.1) for lung lesions; and 59.2% (95% CI 47.3-70.4) for lymph nodes. In conclusion, bone and distant lymph node metastases were reported more often by [18F]FDG-PET/CT than CE-CT, while liver and lung metastases were reported more often by CE-CT than [18F]FDG-PET/CT. Agreement between scans was highest for bone and liver lesions and lowest for lymph node metastases.

A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [18F]FDG PET/CT: a pathology-controlled study

OBJECTIVES

Initial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [¹⁸F]FDG PET/CT provides insufficient and variable diagnostic performance. We aimed to develop and validate a machine-learning-based combination of criteria on [¹⁸F]FDG PET/CT to accurately identify pelvic LN involvement in bladder cancer patients.

METHODS

Consecutive patients with localized MIBC who performed preoperative [¹⁸F]FDG PET/CT between 2010 and 2017 were retrospectively assigned to training (n = 129) and validation (n = 44) sets. The reference standard was the pathological status after extended pelvic LN dissection. In the training set, a random forest algorithm identified the combination of criteria that best predicted LN status. The diagnostic performances (AUC) and interrater agreement of this combination of criteria were compared to a consensus of experts.

RESULTS

The overall prevalence of pelvic LN involvement was 24% (n = 41/173). In the training set, the top 3 features were derived from pelvic LNs (SUVmax of the most intense LN, and product of diameters of the largest LN) and primary bladder tumor (product of diameters). In the validation set, diagnostic performance did not differ significantly between the combination of criteria (AUC = 0.59 95%CI [0.43-0.73]) and the consensus of experts (AUC = 0.64 95%CI [0.48-0.78], p = 0.54). The interrater agreement was equally good with Κ = 0.66 for both.

CONCLUSION

The developed machine-learning-based combination of criteria performs as well as a consensus of experts to detect pelvic LN involvement on [¹⁸F]FDG PET/CT in patients with MIBC.

KEY POINTS

• The developed machine-learning-based combination of criteria performs as well as experts to detect pelvic LN involvement on [¹⁸F]FDG PET/CT in patients with muscle-invasive bladder cancer. • The top 3 features to predict LN involvement were the SUVmax of the most intense LN, the product of diameters of the largest LN, and the product of diameters of the primary bladder tumor.

Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.

State of the Art in 2022 PET/CT in Breast Cancer: A Review

Molecular imaging with positron emission tomography is a powerful and well-established tool in breast cancer management. In this review, we aim to address the current place of the main PET radiopharmaceuticals in breast cancer care and offer perspectives on potential future radiopharmaceutical and technological advancements. A special focus is given to the following: the role of ¹⁸F-fluorodeoxyglucose positron emission tomography in the clinical management of breast cancer patients, especially during staging; detection of recurrence and evaluation of treatment response; the role of 16α-¹⁸Ffluoro-17β-oestradiol positron emission tomography in oestrogen receptors positive breast cancer; the promising radiopharmaceuticals, such as ⁸⁹Zr-trastuzumab and ⁶⁸Ga- or ¹⁸F-labeled fibroblast activation protein inhibitor; and the application of artificial intelligence.

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor

Background

In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images.

Methods

A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model.

Results

The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively).

Conclusion

The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.

Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine

In today's world, progressively enormous popularity prevails around artificial intelligence (AI). AI is gaining popularity in the identification of various images. Therefore, it has been widely used in the ultrasound of the breast. Furthermore, AI can perform a quantitative evaluation, which further helps maintain the diagnosis's accuracy. Moreover, breast cancer is the most common cancer in women, posing a severe threat to women's health. Hence, its early detection is usually associated with a patient's prognosis. As a result, using AI in breast cancer screening and detection is highly crucial. The concept of AI in the perspective of breast ultrasound has been highlighted in this brief review article. It tends to focus on early AI, i.e., traditional machine learning and deep learning algorithms. Also, the use of AI in ultrasound and the use of it in mammography, magnetic resonance imaging, nuclear medicine imaging, and classification of breast lesions is broadly explained, along with the challenges faced in bringing AI into daily practice.

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

Application of Deep Learning in Breast Cancer Imaging

This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as well as monitoring and evaluating breast cancer during treatment. The most commonly used modalities for breast imaging are digital mammography, digital breast tomosynthesis, ultrasound and magnetic resonance imaging. Nuclear medicine imaging techniques are used for detection and classification of axillary lymph nodes and distant staging in breast cancer imaging. All of these techniques are currently digitized, enabling the possibility to implement deep learning (DL), a subset of Artificial intelligence, in breast imaging. DL is nowadays embedded in a plethora of different tasks, such as lesion classification and segmentation, image reconstruction and generation, cancer risk prediction, and prediction and assessment of therapy response. Studies show similar and even better performances of DL algorithms compared to radiologists, although it is clear that large trials are needed, especially for ultrasound and magnetic resonance imaging, to exactly determine the added value of DL in breast cancer imaging. Studies on DL in nuclear medicine techniques are only sparsely available and further research is mandatory. Legal and ethical issues need to be considered before the role of DL can expand to its full potential in clinical breast care practice.

Artificial intelligence for nuclear medicine in oncology

As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.

Intraoperative pathologically-calibrated diagnosis of lymph nodes involved by breast cancer cells based on electrical impedance spectroscopy; a prospective diagnostic human model study

BACKGROUND

Nodal status evaluation is a crucial step in determining prognostic factors and managing treatment strategies for breast cancer patients. Preoperative (CNB), intraoperative (SLNB), and even postoperative techniques (Formalin-Fixed Paraffin-Embedded sectioning, FFPE) have definite limitations of precision or sometimes are time-consuming for the result declaration. The primary purpose of this prospective study is to provide a precise complementary system for distinguishing lymph nodes (LNs) involved by cancerous cells in breast cancer patients intraoperatively.

METHODS

The proposed system, Electrical Lymph Scoring(ELS), is designed based on the dielectric properties of the under-test LNs. The system has a needle-shaped 2-electrode probe entered into SLNs or ALNs dissected from patients through standard surgical guidelines. Impedance magnitude in f = 1 kH (Z_1kHz) and Impedance Phase Slope in frequency ranges of 100 kHz-500 kHz (IPS) were then extracted from the impedance spectroscopy data in a cohort study of 77 breast cancer patients(totally 282 dissected LNs) who had been undergone surgery before (n = 55) or after (n = 22) chemical therapies (non-neoadjuvant or neoadjuvant chemotherapy). A new admittance parameter(Y_n^') also proposed for LN detection in neoadjuvant chemotherapy patients.

RESULTS

Considering the permanent pathology result as the gold standard checked by two independent expert pathologists, a significant correlation was observed between the presence of cancerous cells in LNs and individual ranges of the ELS electrical responses. Compared with normal LNs containing fatty ambient and immune cells, LNs involved by cancerous clusters would reduce the Z_1kHz and increase the IPS. These changes correlate with fat metabolism by cancer cells due to their Fatty Acid Oxidation (FAO) in LN, which results in different dielectric properties between high and low-fat content of normal and cancerous LNs, respectively.

CONCLUSIONS

By finding the best correlation between our defined impedimetric parameters and pathological states of tested LNs, a real-time intraoperative detection approach was developed for highly-sensitive (92%, P<0.001) diagnosis of involved sentinel or axillary LNs. The impact of real-time intraoperative scoring of SLNs would make a pre-estimation about the necessity of excising further LNs to help the surgeon for less invasive surgery, especially in the absence of frozen-section equipment.

Diagnostic Performance of FDG-PET/CT Scan as Compared to US-Guided FNA in Prediction of Axillary Lymph Node Involvement in Breast Cancer Patients

Purpose

The aim of this study was to evaluate the diagnostic ability of 2-deoxy-2-[fluorine-18]fluoro-d-glucose (¹⁸F-FDG) PET/non-contrast CT compared with those of ultrasound (US)-guided fine needle aspiration (FNA) for axillary lymph node (ALN) staging in breast cancer patients.

Patients and Methods

Preoperative ¹⁸F-FDG PET/non-contrast CT was performed in 268 women with breast cancer, as well as ALN dissection or sentinel lymph node (SLN) biopsy. One hundred sixty-four patients underwent US-guided FNA in combination with ¹⁸F-FDG PET/CT. The diagnostic performance of each modality was evaluated using histopathologic assessments as the reference standard. The receiver operating characteristic (ROC) curves were compared to evaluate the diagnostic ability of several imaging modalities.

Results

Axillary ¹⁸F-FDG uptake was positive in 180 patients, and 125 patients had axillary metastases according to the final pathology obtained by ALN dissection and/or SLN dissection. Of the patients with positive ¹⁸F-FDG uptake in the axilla, 21% had false-positive results, whereas 79% were truly positive. Eighty-eight patients had negative ¹⁸F-FDG uptake in the axilla, among which 25% were false-negative. ¹⁸F-FDG-PET/CT had a sensitivity of 86.59% and a specificity of 63.46% in the assessment of ALN metastasis; on the other hand, US-guided FNA had a sensitivity of 91.67% and a specificity of 87.50%. The mean primary cancer size (p = 0.04) and tumor grade (p = 0.04) in combination were the only factors associated with the accuracy of ¹⁸F-FDG PET/CT for detecting metastatic ALNs.

Conclusion

The diagnostic performance of ¹⁸F-FDG PET/CT for the detection of axillary node metastasis in breast cancer patients was not significantly different from that of US-guided FNA. Combining ¹⁸F-FDG PET/CT with US-guided FNA or SLN biopsy could improve the diagnostic performance compared to ¹⁸F-FDG PET/CT alone.

Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer

We investigated whether textural parameters of peritumoral breast adipose tissue (AT) based on F-18 fluorodeoxyglucose (FDG) PET/CT could predict axillary lymph node metastasis in patients with breast cancer. A total of 326 breast cancer patients with preoperative FDG PET/CT were retrospectively enrolled. PET/CT images were visually assessed and the maximum FDG uptake of axillary lymph nodes (LN SUVmax) was measured. From peritumoral breast AT, 38 textural features of PET imaging were extracted. The diagnostic ability of PET based on visual analysis, LN SUVmax, and textural features of peritumoral breast AT for predicting axillary lymph node metastasis were assessed using the area under the receiver operating characteristic curve (AUC) values. Among the 38 peritumoral breast AT textural features, grey-level co-occurrence matrix (GLCM) entropy showed the highest AUC value (0.830) for predicting axillary lymph node metastasis. The value of GLCM entropy was higher than that of visual analysis (0.739; p < 0.05) and the AUC value was comparable to that of LN SUVmax (0.793; p > 0.05). In the subgroup analysis of patients with negative findings on visual analysis, GLCM entropy still showed a high diagnostic ability (AUC: 0.759) in predicting lymph node metastasis. The findings suggest a potential diagnostic role of PET/CT imaging features of peritumoral breast AT in predicting axillary lymph node metastasis in patients with breast cancer.